Social Bias in AI: Re-coding Innovation through Algorithmic Political Capitalism

Abstract This research examines the social dynamics underpinning algorithmic bias, proposing a framework for addressing these issues through the lens of algorithmic political capitalism. We explore how socio-technical-ecological relations of power often reproduce harmful algorithmic effects, including social bias, data exploitation in the knowledge economy, prejudiced predictions, and unexamined user biases that obscure power asymmetries and harm society. Building on complexity theory, particularly Morçöl’s definition of public policy as a dynamic system with co-evolving relationships between actors and systems, we analyze the challenges and opportunities to mitigate these harms within a multilayered framework. Our framework extends Keller and Block’s concept of ‘technology-dependent political capitalism’, incorporating mechanisms to ensure government assistance is conditional, allowing bicameral governance in supported corporations, and empowering local and state authorities to hold organizations accountable. Finally, we highlight the crucial roles of transparency, accountability, and democratization in fostering meaningful innovation, and argue that addressing algorithmic bias and the inequities of the knowledge economy requires a nuanced understanding of the interplay between public policy, technological systems, and societal structures. Our proposals aim to reshape the socio-technical-ecological landscape, creating conditions for algorithmic innovation that align with democratic values and equitable societal progress, while mitigating systemic violence.